Application of machine learning for NDVI-based erosion zone segmentation

Mukhammed Bolsynbek (), Gulzira Abdikerimova (), Zhazira Taszhurekova (), Rysty Tazhiyeva () and Madi Akhmetzhanov ()

International Journal of Innovative Research and Scientific Studies, 2025, vol. 8, issue 3, 2431-2437

Abstract: This paper discusses the application of machine learning methods for the automatic detection of erosion zones based on the NDVI index. The U-Net model with the EfficientNetB0 pre-trained encoder is used, which allows us to achieve high segmentation accuracy. The study includes the preparation and analysis of geospatial data, model training, and testing on data from the region of South Kazakhstan. The developed system demonstrates an accuracy of 99.99%, which confirms the effectiveness of the proposed methodology. The obtained results can be used for monitoring soil degradation and taking measures to prevent erosion processes.

Keywords: EfficientNetB0; Geospatial data; Image segmentation; Machine learning; NDVI; Remote sensing; Soil erosion; U-Net. (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:aac:ijirss:v:8:y:2025:i:3:p:2431-2437:id:7021

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